Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The acoustic-modeling problem in automatic speech recognition
The acoustic-modeling problem in automatic speech recognition
Improved vocabulary-independent sub-word HMM modelling
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
An investigation of PLP and IMELDA acoustic representations and of their potential for combination
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
The Lincoln tied-mixture HMM continuous speech recognizer
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Acoustic-phonetic transformations for improved speaker-independent isolated word recognition
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Improvements in connected digit recognition using linear discriminant analysis and mixture densities
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Exploiting prediction error in a predictive-based connectionist speech recognition system
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Front end analysis of speech recognition: a review
International Journal of Speech Technology
International Journal of Speech Technology
Integration of multiple acoustic and language models for improved Hindi speech recognition system
International Journal of Speech Technology
Discriminative feature extraction for speech recognition using continuous output codes
Pattern Recognition Letters
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
A Family of Discriminative Manifold Learning Algorithms and Their Application to Speech Recognition
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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The interaction of Linear Discriminant Analysis (LDA) and a modeling approach using continuous Laplacian mixture density HMMs is studied experimentally. The largest improvements in speech recognition accuracy could be obtained when the classes for the LDA transform were defined to be sub-phone units. On a 12,000-word German recognition task with small overlap between training and test vocabulary a reduction in error rate by one fifth was achieved compared to the case without LDA. On the development set of the DARPA RM1 task the error rate was reduced by one third. For the DARPA speaker-dependent nogrammar case, the error rate averaged over 12 speakers was 9.9%. This was achieved with a recognizer employing LDA and a set of only 47 Viterbi-trained contextindependent phonemes.